general_options = {"xlabel": "time (seconds)", "ylabel": "Amplitude"}
sawtooth_line_options = [{"label": f"N = {n}"} for n in Ns]
sawtooth_line_options.append({
"label": "True signal",
"linewidth": 2.5,
"color": "black"
})
sq_line_options = [{"label": f"N = {n}"} for n in Ns]
sq_line_options.append({
"label": "True signal",
"linewidth": 2,
"color": "black"
})
# Encapsulate and Plot signals
sawtooth_fig = FigData(
ts,
sawtooths,
line_options=sawtooth_line_options,
title="Sawtooth wave approximated with a Fourier series",
**general_options)
square_fig = FigData(ts,
squares,
line_options=sq_line_options,
title="Square wave approximated with a Fourier series",
**general_options)
aplot.single_plot(sawtooth_fig)
aplot.single_plot(square_fig)
fft_stack_fig = FigData(xs=x,
ys=y,
zs=[np.abs(np.array(ws_ffts))**2],
title="STFT surface from stacking FFT",
plot_type="plot_surface",
xlabel="Frequency (Hz)",
ylabel="Time (s)",
zlabel="Normalised Amplitude",
line_options=[{"cmap": "viridis"}],
options=["invert_xaxis"],
figsize=(6, 5),
colorbar_params={"pad": 0.1,
"vmax": 0.25})
aplot.single_plot(fft_stack_fig,
auto_timestamp=False,
folder="stft_schematic",
savefig_path="fft_stack.png"
)
# Plot the original signal and the STFT
signal_fig = FigData(xs=nst.samp_nums,
ys=[nst.data],
title="Non stationary signal",
plot_type="plot",
xlabel="Time (s)",
ylabel="Amplitude",
figsize=(5, 2.5))
aplot.single_plot(signal_fig,
auto_timestamp=False,
folder="stft_schematic",
# savefig_path="signal.png"
f"STFT Magnitude (Zoomed) of {scale_name} Major scale\n(Artificial clarinet sound)",
line_options=[{
"vmin": 0,
"vmax": Zxx_max,
"shading": 'gouraud'
}],
options=["grid"],
plot_type="pcolormesh",
xlabel="Time (s)",
ylabel="Frequency (Hz)",
fit_data=False)
#
# Display results
#
aplot.single_plot(fig_data=signal_fig)
aplot.single_subplots(
grid_size=(2, 2),
fig_data={
(0, 0): fft_fig,
(1, 0): fft_zoomed_fig,
(0, 1): stft_fig,
(1, 1): stft_zoomed_fig
},
individual_figsize=(6, 4),
savefig_path=f"STFT_Clarinet_{scale_name}_Major_scale_sampr={samp_rate}")
# wav.write(f"audio/cl_{scale_name}_scale", nst)
#
# Generate data
#
signals = [
ss.SineSignal(freq=freqs[i],
duration=durations[i],
chop_range=chop_ranges[i]) for i in range(len(freqs))
]
nst = nsts.NonStationarySignal(signals)
#
# Encapsulate data in FigData
#
fig = FigData(xs=nst.samp_nums,
ys=nst.data,
title="Non stationary signal",
plot_type="plot",
xlabel="Time (s)",
ylabel="Amplitude")
#
# Plot
#
aplot.single_plot(fig)
#
# Output audio
#
wav.write("audio/nonst8", nst, dtype=np.uint8)
duration = 0.01
samp_rate = 44100
cl_f = 466.16 / 2 # written pitch C4, actual pitch is Bb4
cl_pitch_label = "C4"
signal_params = {
"duration": duration,
"samp_rate": samp_rate,
"freq": cl_f,
"options": {
"normalize": True
}
}
# Generate clarinet signal using predefined amplitudes
cl_signal = pcs.ClarinetApproxSignal(**signal_params)
# Figure options
cl_fig = FigData(xs=cl_signal.samp_nums,
ys=[cl_signal.data],
line_options=[{"label": f"f0 = {cl_f}Hz"}],
xlabel="Time (seconds",
ylabel="Amplitude",
title=f"Synthesized clarinet (written pitch {cl_pitch_label})",
figsize=(5, 3)
)
# Plot signals
aplot.single_plot(cl_fig)